@article{LeiseEsserEichenlaubetal.2021,
  author    = {Leise, Philipp and Eßer, Arved and Eichenlaub, Tobias and Schleiffer, Jean-Eric and Altherr, Lena and Rinderknecht, Stephan and Pelz, Peter F.},
  title     = {Sustainable system design of electric powertrains - comparison of optimization methods},
  series = {Engineering Optimization},
  journal   = {Engineering Optimization},
  publisher = {Taylor \& Francis},
  address   = {London},
  issn      = {0305-215X},
  doi       = {10.1080/0305215X.2021.1928660},
  year      = {2021},
  abstract  = {The transition within transportation towards battery electric vehicles can lead to a more sustainable future. To account for the development goal 'climate action' stated by the United Nations, it is mandatory, within the conceptual design phase, to derive energy-efficient system designs. One barrier is the uncertainty of the driving behaviour within the usage phase. This uncertainty is often addressed by using a stochastic synthesis process to derive representative driving cycles and by using cycle-based optimization. To deal with this uncertainty, a new approach based on a stochastic optimization program is presented. This leads to an optimization model that is solved with an exact solver. It is compared to a system design approach based on driving cycles and a genetic algorithm solver. Both approaches are applied to find efficient electric powertrains with fixed-speed and multi-speed transmissions. Hence, the similarities, differences and respective advantages of each optimization procedure are discussed.},
  language  = {en}
}